Elements of information theory
Elements of information theory
On the learnability of discrete distributions
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
On the learnability and usage of acyclic probabilistic finite automata
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Evolutionary Trees Can be Learned in Polynomial Time in the Two-State General Markov Model
SIAM Journal on Computing
When Can Two Unsupervised Learners Achieve PAC Separation?
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
PAC-learnability of Probabilistic Deterministic Finite State Automata
The Journal of Machine Learning Research
PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance
Theoretical Computer Science
Towards Feasible PAC-Learning of Probabilistic Deterministic Finite Automata
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
| Hi-index | 0.00 |
We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the generation of strings of symbols, that have been used in the context of speech and handwriting recognition, and bioinformatics. Recent work on learning PDFAs from random examples has used the KL-divergence as the error measure; here we use the variation distance. We build on recent work by Clark and Thollard, and show that the use of the variation distance allows simplifications to be made to the algorithms, and also a strengthening of the results; in particular that using the variation distance, we obtain polynomial sample size bounds that are independent of the expected length of strings.